Why is web3AI infra construction more difficult than expected?

Author: Haotian

1) At present, most of the active AI projects in web3 are generally MEME-like, boasting a lot of stories that cannot be realized and landed. The key is to attract most of the attention and liquidity by quickly issuing coins to enter the market, as well as the mess after the short-term bubble burst (negative EV). Mainly because the narrative of AI + Crypto is too sexy, and its actual landing application challenges are too great, it naturally became a bubble disaster area that relies on narrative to issue coins from the beginning;

2) web3AI infra is essentially a reconstruction of web2 AI infra, and most of the time it is thankless. Just like when Crypto challenged centralization in the name of decentralization, for a long time, decentralized network architecture was criticized for repeated construction and meaninglessness, until the subsequent landing of DeFi application scenarios found some value capture points.

The current dilemma of web3AI is no different from the original vision of decentralized Crypto. Most people are still used to saying "what's the use of web3AI"? But don't forget that decentralized computing power aggregation, distributed reasoning, and distributed data annotation networks can all find entry scenarios in terms of training cost, performance, and practicality. It can only be said that the road ahead is long and difficult, but of great significance; 3) The cost of building and expanding the trial and error period of web3AI infra is relatively high, and requires strong rational support. For example, everyone knows that web3AI needs to build a data layer, but cleaning huge on-chain and off-chain data requires a lot of server operation and maintenance and development costs. At the same time, the cost of mature web3AI API access, computing power, and algorithm fine-tuning also require costs. If these cost investments are focused on Agent applications, commercial monetization models can be quickly explored, but if they are focused on the infra level, in the current market context where technical narratives are not so popular, it is a challenge for many developer teams.

What's more troublesome is that, unlike traditional web2 infrastructure, web3 AI also has to solve the coordination problem of off-chain data and on-chain verification, the model distribution and update mechanism under the P2P network, and the complex design of replacing the traditional business model with Tokenomics incentives, etc. The short-sightedness of capital and the market's preference for speculation have caused some hot money to flow into Agent applications that were hastily launched purely for the sake of hot spots, resulting in the teams that are really working on the infrastructure layer being difficult to get enough support.

4) The illusion problem of large models compatible with the "black box" attribute of web3AI infra makes its security and credibility challenges in specific scenarios huge. Seeing @SlowMist_Team's recent output on MCP security vulnerabilities, it feels that the professional security audit around MCP can already support SlowMist's future positioning as an AI audit company. This is just a concrete case that verifies the various unknown security challenges of AI LLMs as a basic data source connected to web3 AI infra. But the problems surrounding web3 AI infra are far more than these. In addition, there is a verifiable computing framework built through web3 cryptographic verification and on-chain consensus mechanism to ensure that the AI ​​reasoning process can be traced and verified.

In fact, the trusted verification and computing framework of AI are the core areas that web3AI infra needs to conquer. When the current large models process highly sensitive information such as finance, medical care, and law, the adoption rate in professional fields is greatly limited because they cannot provide verifiability of the reasoning process. The maturity of web3 AI infra, such as the zkVM bottom layer, decentralized Oracle network, decentralized Memory solution, etc., can build a verifiable and provable computing framework for AI, fundamentally helping AI to achieve rapid expansion of vertical scenarios.

The above.

The journey of web3AI infra construction and application construction will not be achieved overnight, but a long marathon. Whoever can truly build an infra and application ecosystem that solves real problems, whoever can balance the relationship between hype and value in the Go-To-Market process, and whoever can find a practical business closed loop while maintaining technological foresight, will be the one who truly has the last laugh in the industry.